Portfolio analysis often starts with returns, volatility, drawdowns, and correlations. But price history alone does not describe the liquidity conditions, trading activity, or market states surrounding those results.
FinFeedAPI provides T+1 historical OHLCV and market metadata across 40+ exchanges, with detailed Native IEX historical microstructure data for supported symbols and dates. Portfolio teams can use these datasets to support historical performance research, risk analysis, liquidity investigation, and asset-behavior studies across different historical periods.
Returns provide an essential view of portfolio performance. But they compress everything that happened during each observation period into a relatively small set of numbers.
For deeper historical analysis, portfolio teams may also need to understand liquidity conditions, trading activity, session timing, and unusual market states around periods of interest.
Volatility, drawdowns, and other common risk measures are often calculated primarily from historical price observations.
Price and volume alone provide limited information about historical spreads and displayed market depth.
Relationships calculated over one historical period may look different during another, requiring analysis across multiple windows and market environments.
Regular market hours, extended periods, auctions, halts, and other market states can create very different historical contexts.
A return series can show when a portfolio gained or lost value without showing the exchange-level activity surrounding those movements.
Suppose several assets in a portfolio decline sharply during the same historical period.
Returns can show the size of the drawdown and how assets moved relative to one another. A deeper investigation might ask:
These observations do not determine whether a portfolio was correctly constructed or establish the cause of its performance. They provide additional historical evidence for portfolio research.
For multi-market portfolios, teams should account for currency normalization and any required FX methodology within their own analytics workflow.
FinFeedAPI provides T+1 historical OHLCV across 40+ exchanges through a standardized interface. Portfolio teams can use historical OHLCV, including adjusted data where available and appropriate, as inputs for calculating returns, volatility, drawdowns, correlations, and other portfolio metrics according to their own methodology.
Native IEX Level 1 provides historical best bid, best ask, and associated sizes for supported symbols and dates. This allows researchers to examine historical top-of-book conditions alongside periods of portfolio gains, losses, or elevated volatility.
Native IEX Level 2 provides displayed liquidity across multiple price levels. Portfolio researchers can use these observations to study how visible depth differed across selected historical periods.
Native IEX Level 3 provides individual historical order book lifecycle events. For deeper research, teams can examine supported order additions, modifications, deletions, executions, and book-clearing activity around periods of interest.
Native IEX system events provide information including system hours, regular market hours, message boundaries, and session-state transitions. Administrative messages provide additional symbol-level context including trading status, auctions, operational halts, official open/close prices, and other relevant events.
REST and JSON-RPC provide structured access to historical Stock API resources. Teams can define instruments, historical windows, and required datasets consistently across portfolio research, performance review, and reporting workflows.
Use historical OHLCV, with appropriate adjustments where required, as an input for calculating returns, volatility, drawdowns, rolling statistics, and other portfolio-level metrics. Holdings, weights, cash flows, fees, rebalancing, corporate actions, and other portfolio-specific inputs remain part of the team's own methodology.
Analyze correlations or other statistical relationships across different historical windows rather than relying on a single full-period estimate. Researchers can compare how those relationships changed during calm, volatile, or otherwise unusual historical periods.
Identify a significant historical drawdown and examine the individual assets contributing to it. For supported Native IEX instruments, researchers can then investigate trades, spreads, displayed liquidity, and order book activity around the same period.
Native IEX Level 1 and Level 2 data can support research into historical spreads, quoted sizes, and displayed depth. This can provide additional evidence when evaluating periods where liquidity assumptions are important to the analysis.
System events and administrative messages can help distinguish regular market hours from other exchange states and identify supported auctions, halts, or trading-status changes. Portfolio teams can use this context when defining or comparing historical observation windows.
Start with a portfolio-level observation such as elevated volatility or a drawdown, then investigate supported Native IEX microstructure data for relevant instruments. This creates a path from portfolio-level metrics to deeper historical exchange-level investigation.
OHLCV provides the foundation for many portfolio calculations. Supported Native IEX datasets can add exchange-level microstructure context when deeper investigation is required.
| Data | What It Provides | How It Can Support Portfolio Analysis |
|---|---|---|
| OHLCV | T+1 historical open, high, low, close, and volume across 40+ exchanges | Inputs for returns, volatility, drawdowns, correlations, performance research, and historical comparisons |
| Market Metadata | Exchange and symbol information | Instrument discovery and consistent organization of portfolio datasets |
| Trades | Individual historical trades and supported condition flags | Trade-level investigation around selected portfolio periods |
| Level 1 | Historical best bid, best ask, and associated sizes | Historical spread and top-of-book analysis |
| Level 2 | Displayed liquidity across price levels | Depth and liquidity-distribution research |
| Level 3 | Individual order book lifecycle events | Detailed investigation of historical book activity |
| System Events | System hours, regular market hours, message boundaries, and session-state transitions | Session segmentation and exchange-state context |
| Administrative Messages | Trading status, auctions, operational halts, official open/close prices, and other symbol-level information | Context around unusual periods and symbol-level events |
OHLCV and market metadata provide the broadest foundation for portfolio research. Trades, Level 1, Level 2, Level 3, system events, and administrative messages described here are Native IEX historical datasets for supported symbols and dates.
| What the Portfolio Workflow Needs | Before | With FinFeedAPI |
|---|---|---|
| Historical market inputs | Portfolio research may rely on limited or inconsistent historical price sources | T+1 OHLCV provides standardized historical price and volume observations across 40+ exchanges |
| Risk research | Volatility and drawdowns may be studied with limited additional market context | Historical price metrics can be investigated alongside supported trade and microstructure data |
| Correlation research | Relationships may be calculated over broad periods without comparing different environments | Consistent historical data supports comparison across different windows and selected market periods |
| Spread analysis | Historical spreads may be unavailable or approximated | Native IEX Level 1 provides historical bid/ask observations |
| Liquidity analysis | Liquidity may be inferred mainly from price and volume | Native IEX Level 2 provides displayed liquidity across price levels |
| Detailed market activity | Individual order book activity is unavailable from portfolio returns | Native IEX Level 3 provides historical order book lifecycle events |
| Market-state context | Different periods may be compared without exchange-state information | System events and administrative messages provide additional historical context |
| Repeatable research | Portfolio analysis may require multiple data sources and retrieval processes | Structured API access supports consistently defined historical workflows |
FinFeedAPI supplies market-data inputs rather than a complete portfolio accounting system. Holdings, weights, corporate actions, cash flows, fees, currency conversion, and other portfolio-specific calculations remain part of the customer's workflow.
FinFeedAPI provides structured interfaces for retrieving historical stock data for portfolio research, historical risk analysis, performance review, reporting, and analytical applications.
REST works well for portfolio analytics platforms, research notebooks, scheduled calculations, dashboards, and historical data pipelines.
Teams can request supported historical data for defined instruments and periods and perform portfolio calculations within their own infrastructure.
JSON-RPC provides structured RPC-based access for supported historical stock workflows.
It can be integrated into portfolio analytics systems, research infrastructure, and automated historical analysis.
FinFeedAPI also provides a hosted Stock Historical MCP server.
Supported tools for exchange and symbol discovery, OHLCV, and Native IEX historical resources can be used in compatible AI-assisted portfolio research and analytical workflows.
Portfolio analytics might identify:
The investigation can continue:
For portfolios spanning multiple currencies, the analysis should also apply the team's chosen FX normalization methodology before interpreting portfolio-level performance.
The portfolio metric identifies the period worth studying. Historical market data provides additional observations that can be used to investigate it.
Start with T+1 historical OHLCV as a market-data input for portfolio return and risk analysis, then add supported Native IEX trades, spreads, liquidity, order book activity, and market-state context when deeper investigation is required.
FinFeedAPI gives portfolio teams a structured historical data foundation for research, performance review, reporting, and market-context analysis.
